889 resultados para Verbal symbolic reasoning


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Abstract


AIMS/HYPOTHESIS:

Retinal vascular calibre changes may reflect early subclinical microvascular disease in diabetes. Because of the considerable homology between retinal and cerebral microcirculation, we examined whether retinal vascular calibre, as a proxy of cerebral microvascular disease, was associated with cognitive function in older people with type 2 diabetes.

METHODS:

A cross-sectional analysis of 954 people aged 60-75 years with type 2 diabetes from the population-based Edinburgh Type 2 Diabetes Study was performed. Participants underwent standard seven-field binocular digital retinal photography and a battery of seven cognitive function tests. The Mill Hill Vocabulary Scale was used to estimate pre-morbid cognitive ability. Retinal vascular calibre was measured from an image field with the optic disc in the centre using a validated computer-based program.

RESULTS:

After age and sex adjustment, larger retinal arteriolar and venular calibres were significantly associated with lower scores for the Wechsler Logical Memory test, with standardised regression coefficients -0.119 and -0.084, respectively (p?<?0.01), but not with other cognitive tests. There was a significant interaction between sex and retinal vascular calibre for logical memory. In male participants, the association of increased retinal arteriolar calibre with logical memory persisted (p?<?0.05) when further adjusted for vocabulary, venular calibre, depression, cardiovascular risk factors and macrovascular disease. In female participants, this association was weaker and not significant.

CONCLUSIONS/INTERPRETATION:

Retinal arteriolar dilatation was associated with poorer memory, independent of estimated prior cognitive ability in older men with type 2 diabetes. The sex interaction with stronger findings in men requires confirmation. Nevertheless, these data suggest that impaired cerebral arteriolar autoregulation in smooth muscle cells, leading to arteriolar dilatation, may be a possible pathogenic mechanism in verbal declarative memory decrements in people with diabetes.

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We address the problem of multi-target tracking in realistic crowded conditions by introducing a novel dual-stage online tracking algorithm. The problem of data-association between tracks and detections, based on appearance, is often complicated by partial occlusion. In the first stage, we address the issue of occlusion with a novel method of robust data-association, that can be used to compute the appearance similarity between tracks and detections without the need for explicit knowledge of the occluded regions. In the second stage, broken tracks are linked based on motion and appearance, using an online-learned linking model. The online-learned motion-model for track linking uses the confident tracks from the first stage tracker as training examples. The new approach has been tested on the town centre dataset and has performance comparable with the present state-of-the-art

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Reasoning about problems with empirically false content can be hard, as the inferences that people draw are heavily influenced by their background knowledge. However, presenting empirically false premises in a fantasy context helps children and adolescents to disregard their beliefs, and to reason on the basis of the premises. The aim of the present experiments was to see if high-functioning adolescents with autism are able to utilize fantasy context to the same extent as typically developing adolescents when they reason about empirically false premises. The results indicate that problems with engaging in pretence in autism persist into adolescence, and this hinders the ability of autistic individuals to disregard their beliefs when empirical knowledge is irrelevant.

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Based on the Dempster-Shafer (D-S) theory of evidence and G. Yen's (1989), extension of the theory, the authors propose approaches to representing heuristic knowledge by evidential mapping and pooling the mass distribution in a complex frame by partitioning that frame using Shafter's partition technique. The authors have generalized Yen's model from Bayesian probability theory to the D-S theory of evidence. Based on such a generalized model, an extended framework for evidential reasoning systems is briefly specified in which a semi-graph method is used to describe the heuristic knowledge. The advantage of such a method is that it can avoid the complexity of graphs without losing the explicitness of graphs. The extended framework can be widely used to build expert systems

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CCTV systems are broadly deployed in the present world. Despite this, the impact on anti-social and criminal behaviour has been minimal. Subject reacquisition is a fundamental task to ensure in-time reaction for intelligent surveillance. However, traditional reacquisition based on face recognition is not scalable, hence in this paper we use reasoning techniques to reduce the computational effort which deploys the time-of-flight information between interested zones such as airport security corridors. Also, to improve accuracy of reacquisition, we introduce the idea of revision as a method of post-processing.We demonstrate the significance and usefulness of our framework with an experiment which shows much less computational effort and better accuracy.

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Smart Spaces, Ambient Intelligence, and Ambient Assisted Living are environmental paradigms that strongly depend on their capability to recognize human actions. While most solutions rest on sensor value interpretations and video analysis applications, few have realized the importance of incorporating common-sense capabilities to support the recognition process. Unfortunately, human action recognition cannot be successfully accomplished by only analyzing body postures. On the contrary, this task should be supported by profound knowledge of human agency nature and its tight connection to the reasons and motivations that explain it. The combination of this knowledge and the knowledge about how the world works is essential for recognizing and understanding human actions without committing common-senseless mistakes. This work demonstrates the impact that episodic reasoning has in improving the accuracy of a computer vision system for human action recognition. This work also presents formalization, implementation, and evaluation details of the knowledge model that supports the episodic reasoning.

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Physical Access Control Systems are commonly used to secure doors in buildings such as airports, hospitals, government buildings and offices. These systems are designed primarily to provide an authentication mechanism, but they also log each door access as a transaction in a database. Unsupervised learning techniques can be used to detect inconsistencies or anomalies in the mobility data, such as a cloned or forged Access Badge, or unusual behaviour by staff members. In this paper, we present an overview of our method of inferring directed graphs to represent a physical building network and the flows of mobility within it. We demonstrate how the graphs can be used for Visual Data Exploration, and outline how to apply algorithms based on Information Theory to the graph data in order to detect inconsistent or abnormal behaviour.